NBER WORKING PAPER SERIES

AFTER THE DROUGHT:THE IMPACT OF MICROINSURANCE ON CONSUMPTION SMOOTHING AND ASSET PROTECTION

Sarah A. JanzenMichael R. Carter

Working Paper 19702http://www.nber.org/papers/w19702

NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue

Cambridge, MA 02138December 2013, Revised February 2018

This publication uses data collected through a collaborative project of the International Livestock Research Institute, Cornell University, Syracuse University and the BASIS Research Program at the University of California at Davis. This project was made possible by the generous funding of the UK Department for International Development through FSD Trust Grant SWD/Weather/43/2009, the United States Agency for International Development grants No: EDH-A-00-06-0003-00 and the World Bank’s Trust Fund for Environmentally and Socially Sustainable Development Grant No: 7156906. The authors wish to thank the following individuals for the critical contributions they made to this research: Stephen Boucher, Sommarat Chantarat, Munenobu Ikegami, Travis Lybbert, Andrés Moya and Andrew Mude. All errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

© 2013 by Sarah A. Janzen and Michael R. Carter. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.

After the Drought: The Impact of Microinsurance on Consumption Smoothing and Asset Protection Sarah A. Janzen and Michael R. CarterNBER Working Paper No. 19702December 2013, Revised February 2018JEL No. G22,O12,O16

ABSTRACT

To cope with shocks, poor households with inadequate access to financial markets can sell assets to smooth consumption and, or reduce consumption to protect assets. Both coping strategies can be economically costly and contribute to the transmission of poverty, yet limited evidence exists regarding the effectiveness of insurance to mitigate these costs in risk-prone developing economies. Utilizing data from an RCT in rural Kenya, this paper estimates that on average an innovative microinsurance scheme reduces both forms of costly coping. Threshold econometrics grounded in theory reveal a more complex pattern:(i) wealthier households primarily cope by selling assets, and insurance makes them 96 percentage points less likely to sell assets following a shock; (ii) poorer households cope primarily by cutting food consumption, and insurance reduces by 49 percentage points their reliance on this strategy.

Sarah A. JanzenDepartment of EconomicsMontana State UniversityBozeman, MN 59717; USAsarah.janzen@montana.edu

Michael R. CarterDepartment of Agricultural and Resource EconomicsUniversity of California, DavisOne Shields AvenueDavis, CA 95616and NBERmrcarter@ucdavis.edu

Poor households in developing rural economies are in many places highly vulnerable,

exposed to climatic and other shocks that can slash incomes and destroy productive assets.

For many, binding credit constraints and missing insurance markets limit coping options to

the sale of remaining assets or to cuts in family consumption, both of which can have serious

long-term economic repercussions. In this paper we assess the impact of a novel satellite-

based microinsurance contract on households’ reliance on these costly coping strategies in

the horn of Africa.

Microinsurance has been heralded over the past decade as a market-based risk trans-

fer mechanism with the potential to act as a safety net, preventing catastrophic collapse.

Although a number of microinsurance pilot projects have appeared in the last few years,

relatively little is known about their impacts. There is a modest body of evidence showing

that microinsurance can influence households’ ex ante resource allocation by encouraging

them to take on riskier, but higher returning activities. However, less is known about the

effectiveness of insurance after a shock is realized for the simple reason that these impacts

are observable only after an insured population receives a shock. This analysis offers one of

the first empirical assessments of the impact of a market-based index insurance contract on

a household’s ability to cope with shocks ex post.

Since 2010, pastoralists in northern Kenya have had the opportunity to purchase an

index-based livestock insurance (IBLI) contract to protect against livestock mortality due

to drought. A harsh drought swept the Horn of Africa in 2011 activating IBLI payouts.

We use households’ reported coping strategies at the time of the payout and randomly

distributed price discount treatments to cleanly identify the impacts of insurance on con-

sumption smoothing and asset protection.

We first consider the average impact of insurance on household coping strategies. Our

results show insurance leads households on average to radically reduce their dependence

on two costly coping strategies that are likely to impair their future productivity. With

insurance, households are on average: (i) 61 percentage points less likely to anticipate selling

livestock in the wake of the 2011 drought, improving their ability to generate income after

drought. (ii) 12 percentage points less likely to reduce meals. Only the former estimated

impact is statistically significant.

There are a number of reasons to expect these averages obscure a more complex pattern of

heterogeneous impacts. In particular, asset poor households are likely to forfeit consumption

(rather than smooth consumption as is often assumed) in times of crisis in order to protect

their limited productive assets and subsequent future income-generating capacity. While we

might expect asset rich households to modestly reduce consumption in response to a shock

that reduces their permanent income, they should in theory be more willing to sell assets in

order to smooth consumption in the wake of a shock.

Motivated by this expectation of bifurcated coping behavior, we employ the Caner and

Hansen (2004) threshold estimation method to provide evidence of a behavioral threshold

in wealth in this setting: consumption smoothing is more common above an estimated

threshold, and asset smoothing is more common below an estimated threshold. This finding,

interesting in and of itself, implies that simply estimating the average effect of insurance

may be misleading. The results of our threshold-based impact analysis show that:

1. Households holding assets above the estimated threshold, who are most likely to sell

assets, are (a statistically significant) 96 percentage points less likely to anticipate doing

so when an insurance payout is available. Insurance has no significant impact on meal

reductions by these predominantly consumption smoothing households.

2. Households holding assets below the estimated critical threshold, who are prone to

destabilizing consumption, are (a statistically significant) 49 percentage points less

likely to anticipate doing so with insurance. Relative to wealthier households, insurance

has a dampened (54 percentage points), yet still significant, impact on asset sales by

these asset smoothing households.

2

Together, these results suggest that insurance can help households to protect assets during

crises, without having a deleterious effect on human capital investments.

The rest of the paper is organized as follows. Section 1 first provides an overview of the

literature studying the impacts of insurance on both ex ante risk management decisions and

ex post risk coping strategies. Section 2 then provides essential background on the research

setting and available data. This is followed by Section 3, which outlines our identification

and estimation strategies and presents our main findings on the impact of insurance on asset

and consumption smoothing strategies. Section 4 concludes.

1 Evidence on the Impacts of Microinsurance

A growing literature has been devoted to studying the benefits of insurance for poor

households in low income countries. This type of insurance (targeted to poor households,

and available at low cost) has become known as microinsurance. Barnett et al. (2008), Dercon

et al. (2008), Miranda and Farrin (2012), Cole et al. (2012) and Jensen and Barrett (2016)

provide summaries of the literature. The literature highlights two primary avenues through

which insurance might bring about positive impacts. These avenues reflect the fact that

households make both ex ante risk management decisions and ex post risk coping decisions.

Most of the empirical literature has focused on the impact of insurance on ex ante invest-

ment decisions. A common response to uninsured risk is to simply allocate resources toward

activities with lower risk despite the fact that these lower-risk activities generally produce a

lower return (Rosenzweig and Binswanger, 1993). There is growing evidence that insurance

encourages investment in higher risk activities with higher expected profits. Mobarak and

Rosenzweig (2012) provide evidence that farmers in India with access to insurance shift into

riskier, but higher-yielding rice production. Cai et al. (2015a) find that insurance for sows

significantly increases farmers’ tendency to raise sows in southwestern China, where sow

3

production is considered a risky production activity with potentially large returns. Karlan

et al. (2014) show that farmers who purchase rainfall index insurance in Ghana increase

agricultural investment. Elabed et al. (2014) find that cooperatives with access to area-

yield index insurance for cotton increased risky cotton production (and subsequent cotton

inputs) in Mali. Cai (2016) demonstrates that tobacco insurance increases the land tobacco

farmers devoted to risky tobacco production by 20% in China. Similarly, Cole et al. (2017)

find rainfall insurance induces farmers to increase land and input usage applied to castor

and groundnut production, two risky cash crops, in India. With regards to IBLI in the

same context we study, Jensen et al. (2017) show insurance increases productivity-increasing

investments in livestock.

While the impacts of insurance on ex ante risk management decisions are important,

few papers are able to empirically assess how an insurance payout directly influences the

ability of poor households to recover after a shock. Ex post, households in the wake of a

shock can choose to draw down assets to defend their consumption standard (consumption

smooth),1 or they can preserve assets and destabilize their consumption (asset smooth). The

findings regarding microinsurance’s impact on consumption smoothing are mixed. Karlan et

al. (2014) show insured agricultural producers in Ghana are less like to have missed a meal,

evidence of improved consumption smoothing. In contrast, Cole et al. (2017) find no evi-

dence of improved consumption smoothing among insured households in India who receive

an insurance payout. With regards to asset smoothing, Bertram-Huemmer and Kraehnert

(2017) and Jensen et al. (2017) demonstrate how IBLI (in Mongolia and Kenya respectively)

helps livestock-rearing households avoid selling livestock in the midst of drought, evidence of

improved asset smoothing. These empirical findings on IBLI support the simulation-based

findings of Chantarat et al. (2017) who demonstrate how, at least for non-poor households,

IBLI is expected to protect livestock-rearing households from potentially harmful asset de-

cumulation.

Our analysis makes an important contribution to this small literature on the ex post

4

impacts of microinsurance. To guide our analysis, we first turn to the rich empirical and

theoretical literature on ex post risk coping strategies. One key message of this literature is

that heterogeneous households respond to shocks differentially, and the differential response

is often tied to wealth. For example, in early empirical work on coping strategies, both

Townsend (1994) and Jalan and Ravallion (1999) note that poor households less effectively

smooth consumption than do wealthier neighbors. In later work, Hoddinott (2006) provides

evidence that in the wake of the 1994-1995 drought in Zimbabwe, richer households sold

livestock in order to maintain consumption. In contrast, poor households with one or two

oxen or cows were much less likely to sell livestock, massively destabilizing consumption

instead. In Ethiopia, Carter et al. (2007) also find evidence of asset smoothing by the poor,

as households coping with a drought attempted to hold onto their livestock at the cost of

consumption. Similarly, Kazianga and Udry (2006) find that poor and wealthy households

in Burkina Faso manage their savings and assets differently in the face of shocks, with poor

households enduring large consumption shorfalls in order to protect livestock. Building on

Kazianga and Udry’s work in Burkina Faso, Carter and Lybbert (2012) show that house-

holds above an estimated asset threshold almost completely insulate their consumption from

weather shocks by drawing down assets, whereas households below the threshold do not,

despite having the assets to do so. While asset smoothing strategies may be instrumentally

rational for some households, they likely come at the cost of immediately reduced consump-

tion, with potentially irreversible losses in child health and nutrition (Carter et al., 2007).2

These empirical findings on differential consumption and asset smoothing are consistent

with a number of theoretical perspectives. For example, a standard asset accumulation model

with a concave production function predicts convergence towards a steady state equilibrium.

In the event that a shock pushes an individual away from the steady state, the standard

model predicts that those further from the steady state will optimally give up consumption

in order to accumulate assets more quickly (asset smoothing). As one approaches the steady

state, she will be more likely to smooth consumption. Multiple equilibrium poverty trap

5

models (e.g., see discussion in Barrett and Carter, 2013), in which accumulation behavior

bifurcates around a critical minimum asset threshold, amplify this asset smoothing logic.

Specifically, for households in the vicinity of this threshold, assets have a strong dynamic

value that incentivizes asset smoothing.

In other words, both standard and poverty trap models indicate that we should expect

consumption and asset smoothing behavior to coexist in a population with strictly positive,

but heterogeneous, levels of assets. In the subsequent analysis, we’ll use our understanding

of heterogeneous responses to shocks to establish a theoretically-driven empirical approach

to study the impacts of microinsurance on ex post risk coping. Of the existing literature

regarding ex post insurance impacts, the Chantarat et al. (2017) simulation-based findings

on the anticipated welfare impacts of IBLI is best able to study heterogeneous treatment

effects. The study predicts IBLI to be most valuable for vulnerable households who have the

most to gain from additional asset protection, with no gains for the poorest households and

only moderate gains for the wealthy. However, the study focuses only on asset accumulation,

ignoring impacts related to the protection of consumption, and relies on strong assumptions

regarding bifurcated asset dynamics. Building on the predictions of the Chantarat et al.

(2017) model, while also relaxing some of the assumptions, we will empirically estimate

heterogeneous impacts of IBLI on both consumption and asset smoothing behaviors.

2 Research Setting and Data

This impact evaluation utilizes data from the index-based livestock insurance (IBLI) pilot

project in northern Kenya’s arid and semi-arid lands (ASALs). This section provides back-

ground information about the research setting, the insurance pilot, and summary statistics

from the available data.

6

Drought Risk and the IBLI Insurance Pilot in Northern Kenya

Prone to periodic climatic shocks, and relatively isolated with sparse financial market

development, northern Kenya and southern Ethiopia are archetypes of rural vulnerability,

as recently witnessed humanitarian crises have shown. With limited alternative produc-

tion technologies, pastoralism represents the primary livelihood in this region, and livestock

the primary productive resource. To manage spatiotemporal variability in forage and water

access, pastoralists in this region regularly and opportunistically move with their herds. Pas-

toralists may also choose to invest in veterinary services, which have been shown to reduce

livestock mortality and herd lactation rates, although such investments are not common. De-

spite these ex ante risk management strategies, when extreme drought strikes this region, the

effects can be devastating. Livestock weaken and die. Households also experience decreases

in current (and future) income flows generated from consumable livestock by-products such

as milk (McPeak, 2004). Ex post, as in other settings, households often have a choice between

protecting consumption standards or selling assets (in this case, selling livestock). However,

both to cope with income losses and to avoid pending mortality loss, distressed sales of live-

stock commonly flood the market, causing downward pressure on livestock prices (Barrett

et al. 2003; Kerven 1992). This further debilitates a pastoralist households’ main productive

resource, making recovery after the drought even more challenging.

Previous research has shown that asset losses in this environment have severe and long-

lasting consequences. Lybbert et al. (2004) and Barrett et al. (2006) use different data and

methods to demonstrate nonlinear asset dynamics in the ASALs, such that when livestock

herds fall below a critical threshold, recovery becomes difficult, and herds tend to move

toward a low level equilibrium. Santos and Barrett (2011) show that access to informal

credit in this region is uneven across households. Poor households are excluded not only

from formal credit markets, but also from informal credit markets, limiting their ability to

borrow for investment thereby prohibiting growth. Toth (2015) argues that these nonlinear

7

asset dynamics stem from a critical herd size necessary to support mobility. Small herds are

restricted to town centers, where rangeland is regularly degraded, limiting herd productivity

and growth. Although broad-based empirical evidence of poverty traps globally has been

mixed (Subramanian and Deaton, 1996; Kraay and McKenzie, 2014), in a recent review,

Kraay and McKenzie (2014) conclude the strongest evidence for poverty traps comes from

rural remote regions like the ASALs of northern Kenya.

In January 2010 the index-based livestock insurance (IBLI) pilot project was launched

in Marsabit District of northern Kenya in an effort to help pastoralists manage drought

risk. Unlike traditional insurance, index-based insurance provides compensation based on

an index rather than realized losses. Doing so eliminates the usual moral hazard and adverse

selection challenges related to insurance while simultaneously reducing the cost of insurance.

The IBLI index, predicted average livestock mortality, was established using longitudinal

observations of household-level herd mortality fit to satellite-based normalized difference

vegetation index (NDVI) measures of available vegetative cover within a particular region.

The IBLI index was originally shown to be highly correlated with actual livestock mortality

losses experienced by pastoralists in the region so that basis risk, the difference between the

index and realized individual losses, was perceived to be minimal, a necessary requirement

for a quality index-based insurance product (see Chantarat et al., 2012 for details regarding

the design and performance of the index).3

The IBLI premium depends on the risk associated with the geographic region in which

the pastoralist household resides (for example, Upper Marsabit is more susceptible to ex-

treme drought than Lower Marsabit, so households insuring in Upper Marsabit pay a higher

premium). Households who wish to insure choose the number of livestock (goats, sheep,

cattle, and camels)4 to insure for a given period. Insured households receive a payout at the

end of each dry season (i.e. at the beginning of October and/or early March) if the predicted

average livestock mortality rate reaches the minimum payout level (15%), with the payout

equal to the value of all predicted losses greater than 15%.

8

In order to study the impact of this insurance, IBLI was rolled out only in randomly

selected districts within Kenya’s arid and semi-arid lands. Within these treatment areas,

households were randomly selected to receive price discounts meant to encourage purchase

of the insurance. As part of this encouragement design, in each sales period 60% of surveyed

households were randomly selected to receive coupons offering a 10-60% discount on the first

15 tropical livestock units (TLUs) insured. Using the identification strategy described in

section 3, our analysis utilizes exogenous variation from coupons distributed for the sales

period in early 2011.

Figure 4.1 depicts fortnightly NDVI averaged across the insured areas of Marsabit district

over the 2010-2011 period in which the drought occurred. The measures are normalized

by their long-term seasonal averages, so that if conditions had been statistically normal,

the NDVI curve would appear in the graph as a horizontal line at zero. As can be seen,

rangeland conditions began to deteriorate in late 2010 with the failure of the “long rains.”

Figure 4.1 shows how the situation deteriorated throughout much of 2011 as a harsh drought

swept across the Horn of Africa. According to the data used for this paper, during this time,

families lost on average more than one third of their animals. The cumulative effect of these

below average conditions triggered the first IBLI payouts in October-November 2011, as

the predicted livestock mortality rose above the 15% deductible in all five insurance zones.

These payouts were made to households who had purchased insurance earlier in the year.

Households in our study received an average payout of about 10,000 Kenyan Shillings, or

roughly $120. This equates to roughly 2/3 the value of a single TLU (or approximately seven

goats) using the average TLU price reported in our data at that time (1 TLU = 15,011

Kenyan Shillings).5 With a median family annual cash income of only $260 in the study

area, these payments were a substantial boost to families’ cash on hand.

Data and Descriptive Statistics

The data available include household-level information collected annually (beginning in

9

2009) for 673 randomly selected households living in various sub-locations across Marsabit

district, all with access to IBLI. In each round of the survey, households were asked to answer

questions about health, education, livestock holdings, herd migration, livelihood activities,

income, consumption, assets, and access to credit. Each household also participated in an

experiment to elicit their risk preferences. In the surveys following the baseline, households

were also asked questions about insurance purchases, access to information about insurance,

and tested on their level of insurance understanding.

The third round of the panel survey coincided with the October-November 2011 IBLI

payout. At that time, every household was asked about the ways in which they had been

coping with the drought over the three months prior to the survey (Q3 of 2011, as shown in

Figure 4.1), and how they anticipated coping with the drought over the 3 months following

the survey (Q4). Specifically, households were asked about reliance each period on common

coping behaviors, including selling livestock, reducing meals and relying on food aid. Insured

households were asked about anticipated fourth quarter coping behavior after the enumerator

told them exactly how much they would receive as an insurance payment. In a few cases,

households had already received the payment prior to the interview. Most received the

payment a week or two after the survey.

The data available for this analysis are thus a mix of conventional reports on behavior

that has already taken place and behavior anticipated to take place in the near future. It

would of course been nice to have also collected data on coping strategies three months after

the insurance payout rather than contemporaneously. However, the remoteness of the area

and finite research budgets rendered such a strategy infeasible. The usefulness of responses

to hypothetical questions for predicting actual behavior has been the source of much debate.

The most widely-used application in economics is contingent valuation (CV) with some ar-

guing that any CV surveys are misleading and their use misguided (Diamond and Hausman,

1994), while others have argued that CV can sometimes be a reliable source, and often the

only source, of important information (Hanemann, 1994; Carson et al., 2001). In social psy-

10

chology, the widely cited theory of planned behavior suggests that behavioral intentions do

indeed result in actual behavior (Ajzen, 1991). Three meta-analyses of empirical evidence

(Albarracin et al., 2001; Sheeran, 2002; Webb and Sheeran, 2006) support the importance

of stated intentions for predicting actual behavior. These studies and others acknowledge

the limitations of using intentions, as do we, but we agree with them that such intentions

are not completely irrelevant for understanding actual behavior. Importantly, reports on

anticipated behavior in quarter 4 of 2011 line up in a sensible way with households’ reports

on their actual behavior in quarter 3 of 2011.

Another reason we might be interested in these results is precisely because they are based

on a household’s expectations for improved or degrading circumstances. The most important

question is arguably, “Does insurance protect households in the midst of a shock (i.e. as

conditions continue to deteriorate)?” If households expect the situation to worsen, then

their response should reflect that. The data suggest this to be the case - 86% of households

anticipated observing some goat or sheep mortality within the herd in the near future, with an

average of 22% mortality anticipated. Similarly, 78% of households anticipated some cattle

mortality, and 67% expected to observe camel mortality despite the perceived resilience of

camels. In reality, predicted livestock mortality (as measured by the IBLI index) across

the region decreased shortly after the payout as the drought lessened and the vegetative

conditions improved. On one hand, this may mean the actual impacts are smaller than our

results reflect. But if our results reflect the impact if the conditions had actually continued

to deteriorate, then they are still relevant from a policy perspective.

Table 1 reports summary statistics on key variables disaggregated by whether a household

was insured during the 2011 drought. All households had the opportunity to insure, but

only 24% had actually purchased insurance. The table provides no evidence that wealthier

individuals (as measured by livestock wealth and a non-livestock asset index6) are more likely

to purchase insurace, although uninsured households are likely to have a higher dependency

ratio (the ratio of children less than 15 years, adults greater than 55 years, disabled or

11

chronically ill household members) and be recipients of a cash transfer targeted to the poorest

households in the region. Education levels, risk attitudes, credit constraints and savings also

vary little between the groups, as do both realized and expected livestock losses. Insured

households are more likely to participate in social groups, which could mean they are more

connected and informed.

While it is perhaps surprising that there are not stronger differences in observable char-

acteristics between those who did and did not purchase insurance, these two groups may

still differ along unobserved dimensions. Table 2 reports values for the discount coupon

treatments that were randomly offered across the entire population. Discount coupons were

effective in boosting up-take. Fully 88% of insurance purchasers received a coupon, whereas

only 55% of non-purchasers received a coupon. The value of the coupon received also dif-

fers sharply between the two groups. As will be discussed in the section that follows, this

encouragement design provides useful instruments for our endogenous regressor (insurance),

being highly correlated with the decision to insure yet uncorrelated with the outcomes of

interest except through purchase of insurance.

Finally, Table 3 previews the coexistence of consumption and asset smoothing in the

northern Kenya study area. The data show the percent of surveyed households that reported

reducing daily meals and selling livestock during the third (Q3) and fourth (Q4) quarters

of the 2011 drought year at the peak of rangeland decline. As can be seen in the first

column of the table, on average, 60 to 70% of households reduced their daily meals, while

less than 30% reported selling livestock in a given quarter to cope with the drought. 95% of

all surveyed households had some livestock that they could in principle have sold had they

wished. Despite holding assets that could be used to smooth consumption, these households

were not smoothing consumption.

While Table 3 shows that asset and consumption smoothing coexist, it also shows that the

relative deployment of these strategies varies by household wealth. Columns 2 and 3 of the

table display the coping strategy employed by households in the lowest and highest quartiles

12

of the livestock wealth distribution. Lower wealth households are roughly 32 percentage

points less likely to sell assets than higher wealth households. While substantial numbers of

higher wealth households report some reliance on meal reduction as a coping strategy (61%

in Q3),7 more than 80% of poorer households rely on this coping strategy. These differences

in means are statistically significant by a standard t-test.

In this context, insurance that indemnifies against large losses would seem to provide

protection for consumption smoothing households against losing productive assets, while af-

fording asset smoothing households a coping strategy that does not impair the human capital

of current and future generations. The last three columns of Table 3 report the difference

between insured and uninsured households’ ability to smooth consumption and assets be-

fore and after receipt of an insurance payout. As can be seen, with the exception of Q3

(pre-payout) livestock sales, insured and uninsured households behave quite differently. The

differences are particularly pronounced for Q4 meal reductions (33% of insured households

report cutting meals, whereas 71% of uninsured households report an intention to rely on

that strategy), and Q4 animal sales (11% versus 32%). While these differences are statis-

tically significant, the key question of course is whether they represent a causal impact, or

simply a spurious correlation induced by the fact that different types of households chose to

purchase insurance. The rest of this paper is dedicated to correctly identifying the causal

relationship between insurance and these costly coping strategies.

3 Estimation Strategy and Results

While the descriptive statistics signal a statistically significant correlation between in-

surance coverage and third and fourth quarter coping strategies, these differences cannot

be given a causal interpretation because the decision to purchase insurance was endogenous

and perhaps correlated with factors expected to independently influence coping strategies.

The goal of this section is to identify the causal impact of insurance by econometrically ex-

13

ploiting a set of randomly distributed encouragements designed to boost insurance uptake.

After explaining the basic identification strategy based on these instruments, we present the

average treatment effect of insurance on household coping strategies. We then lay out a

threshold-based method of testing for the presence of consumption and asset smoothers, and

for the differential impacts of insurance on the behavior of these two groups. We first present

the results utilizing a threshold that has been identified in the empirical literature. We then

employ the Caner and Hansen (2004) GMM threshold estimator to statistically identify the

existence and location of the asset-based threshold.

Estimating the Average Impact of Insurance on Coping Strategies

The analysis of the impacts of insurance would be simplest if we could compare a cohort

of households randomly assigned to an insurance “treatment” with a control group denied

access to insurance. Although IBLI was implemented with a randomized spatial rollout, the

data needed for the analysis here are available only within the treatment area (see Section

2 above). For this analysis we are thus limited only to a population in which all households

had the opportunity to insure their livestock, though not all households chose to do so. Since

households must self-select into purchasing insurance, we must account for selection into the

insurance treatment.

Because the endogenous decision to insure is likely to depend on unobservables, we employ

an instrumental variables (IV) approach similar to Karlan et al. (2014). The encouragement

design implemented with IBLI (as described above) provides two suitable instruments: re-

ceipt and value of an insurance discount coupon received in early 2011. The distribution of

coupons was random, so neither receipt nor value should be correlated with coping strategies

except through the purchase of insurance, but we expect both to be correlated with insurance

uptake. Although some individuals within the same village received vouchers while others

did not, we assume the household’s decision to insure is independent of coupons received (or

not) by others.8

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The descriptive statistics reported in Table 2 suggest that the coupon (both its receipt

and value) is indeed highly correlated with the decision to insure, and thus constitutes a good

instrument. The right hand columns of Table 1 also checks the balance of the covariates,

to ensure that the receipt of the coupon was indeed random. As would be expected, few

statistically significant differences are observed. Coupon recipients are three years younger

on average and are more likely to have participated in social groups. They are also more

likely to report difficulty in acquiring a loan, but equally as likely to have actually taken out

a loan. Less than a quarter of households have any savings, and coupon recipients are less

likely to save. But if they do save, the amount of savings is not significantly different from

non-coupon recipients. As measured by the non-livestock asset index, they do appear to be

slightly less wealthy, but herd size is equivalent across the two populations, and wealth is often

kept as livestock in this region. Despite the fact that coupons were distributed randomly,

a regression of all household characteristics included in Table 2 on coupon receipt suggests

that these variables are jointly significant (F = 4.12). Since the imbalanced variables are

also likely related to coping strategies, in the estimation that follows, we will control for all

characteristics of observable imbalance in our vector of controls Xij as suggested by Bruhn

and McKenzie (2009).

Using IV we obtain the local average treatment effect (LATE) of insurance on coping

strategies. To obtain this effect, we estimate the following first stage regression equation,

where Iij is an indicator variable equal to 1 if household i in location j purchased insurance,

Zij is a vector of instrumental variables (including receipt and value of coupon), Xij is a

vector of covariates that influence a household’s drought-coping behavior, and γj represents

a location fixed effect:9

Iij = Zij δ +Xij θ + γj + vij (3.1)

We then estimate the impact of insurance (β) on ySi, a binary indicator of household i’s use

15

of a particular coping strategy S in the post-insurance payout period, using the following

second stage regression:

ySij = βS Iij +XijφS + γj + εij , (3.2)

where predicted insurance uptake (Iij) is obtained from the first stage estimation 3.1.

As with any encouragement design, there may be some concern that the encouragement

itself induces an artificial selection into the program. Identification stems from those actually

moved by the instrument, and when you change the price of insurance it may incentivize

different types of people. For example, households expecting less benefit from the insurance

may purchase it only because of the discount coupon. This may introduce a downward bias

in estimated impacts relative to the impacts that would occur if coupons had not been used

and only those willing to pay higher prices had purchased the insurance. However, ultimately

evaluating what is or is not bias depends on what the policy relevant treatment effect is.

In this case, the government of Kenya has decided to scale-up IBLI through provision of

free insurance under the Kenya Livestock Insurance Program (KLIP) scheme (Janzen et al.,

2016). As such, it isn’t clear if the policy relevant treatment effect is for the population willing

to pay the market price or those who will only take up insurance when it is provided for free.

By estimating the effect across a range of prices, our LATE estimates appropriately identify

an impact somewhere in between these two extremes. In addition, in this circumstance the

bias may be offset by greater precision. As analyzed in Mullally et al. (2013) and Mullally

(2012), in program evaluations plagued by low participation (such as insurance programs in

which demand has repeatedly been shown to be low), an encouragement design is likely to

substantially reduce the mean square error of impact estimates relative to a research design

based on randomized eligibility.

Moreover, as we will show, the variation in price affects demand much less than the

16

coupon itself, regardless of value. In this context (using a longer time series), Jensen et al.

(2014) estimate relatively inelastic demand (-.43). Other research has shown that barriers

to the uptake of index insurance in similar contexts are often non-price factors including

trust and understanding of the contract, risk aversion, wealth and financial liquidity, and

access to informal risk sharing networks, rather than heterogeneous willingness to pay for

insurance (Gine et al., 2008; Patt et al., 2010; Mobarak and Rosenzweig, 2012; Cole et al.,

2013; McIntosh et al., 2013; Dercon et al., 2014; Cai et al., 2015b). Given the observed price

inelasticity in this context and our understanding of demand for microinsurance in general, we

conclude non-price factors matter most, boosting our confidence that any existing selection

effect is not related to wealth.

Because the assumptions necessary for IV are minimal given the available data, this is

our preferred approach. However, several alternatives to IV exist. Although we do not

discuss or present alternative methods in this paper, we obtain very similar results using

both matching (following an approach similar to Bertram-Huemmer and Kraehnert, 2017)

and Heckman selection methods (see footnotes 10 and 11).

Table 4 presents the first stage regression used to obtain the IV estimates. Column

(1) includes location fixed effects to control for location-specific rangeland conditions. This

approach is used in almost all specifications. Column (2) presents the same first stage

without location fixed effects for reasons described later. The first stage results demonstrate

a strong correlation between receiving a coupon and insurance uptake. It also demonstrates

a minimal (if any) price effect.

Table 5 presents our main impact estimates. We focus on the impact of an insurance

payout on two primary outcomes of interest: anticipated fourth quarter livestock sales and

anticipated reduction in the number of daily meals consumed in quarter 4. Selling livestock

reflects a willingness to destabilize asset holdings, and meal reduction suggests an inability

to smooth consumption. In the first column of each table we present population average

impacts for each outcome of interest using IV as described above. In the following sections

17

we describe our approach for analyzing threshold-disaggregated impacts; these results are

presented in columns (2)-(6) in each table.

Considering first the impact of insurance on curbing the sale of productive assets, the

results presented in the bottom panel of Table 5 suggest that an insurance payout substan-

tially reduces the probability that a household intends to sell livestock. The average impact

results presented in Column (1) of Table 5 imply a large 61 percentage point reduction in

the number of households who anticipated selling livestock in the short run in order to cope

with the 2011 drought.10 As discussed earlier, when poor households endeavor to maintain

scarce productive assets during a shock, it often imposes a high cost on consumption. The

top panel of Table 5 reports the estimated impact of insurance on meal reduction as a coping

strategy. Focusing first on the local average treatment effect in Column (1), an insurance

payout results in a 12 percentage point drop in the number of households that anticipate

decreasing the number of meals eaten each day when under stress from a drought.11 Al-

though the average result for meal reduction is not statistically significant, we show in the

next section that the average results may be masking a heterogenous response, as predicted

by theory. We turn to that analysis now.

Consumption versus Asset Smoothing

As discussed in Section 1, a number of theoretical perspectives suggest that less wealthy

households may hold on to (productive) assets in the wake of a shock rather than liquidate

them to smooth consumption. Table 3 shows that both asset and consumption smoothing

behaviors are observed in the data. The key question is whether all households pursue a

mixed strategy, or whether there are really two distinctive behavioral regimes, as some earlier

work has suggested is likely (e.g., Carter and Lybbert (2012)). In the latter case, estimated

average treatment effects (β in equation 3.2) are a data-weighted average of the behavior in

the two regimes disguising how microinsurance actually impacts a population comprised of

both asset and consumption smoothers.

18

Drawing on the theoretical perspectives summarized in Section 1, we hypothesize that

coping behavior will shift as we move along a wealth or asset continuum. We expect the

relatively asset rich households to largely smooth their consumption by destabilizing their

asset stocks during a shock (excepting for consumption adjustments due to decreases in

permanent income). We would thus expect that microinsurance will result in a reduction

of asset sales for these asset rich households if insurance helps them to better protect their

assets. In contrast, asset poor households would find that the intertemporal value of assets

is extremely high, and thus be unwilling to part with their productive assets even at the cost

of hunger. We would expect that microinsurance will help these asset poor households to

better smooth consumption during a shock, even as they cling to their current asset stocks. In

terms of our measures, insurance should lead to fewer meal reductions for these households,

reducing current hunger and better protecting the human capital of their children.

As can be seen in the descriptive statistics reported in Table 3, data on actual third quar-

ter coping strategies match this expectation of wealth-differentiated coping strategies.12 As

already noted, our quarter 4 outcome variables are measures of intended rather than realized

behavior. Although this reliance on reported intentions is a limitation, its consistency with

the data on actual behavior in the preceding quarter gives confidence in the reliability of the

fourth quarter data.13

While we could potentially devise a specification to test if there is a smooth transition

from asset to consumption smoothing, we follow the lead of earlier empirical (e.g., Hoddinott

(2006), Carter et al. (2007), and Carter and Lybbert (2012)) and theoretical (Zimmerman

and Carter, 2003) work and test for a sharp break in coping behavior along the wealth

continuum using the following model:

ySij =

β`S Iij +Xijφ

`S + γlj + ε`Si if Aij < A∗

βuS Iij +Xijφ

uS + γuj + εuSi if Aij ≥ A∗

(3.3)

where Iij is again the instrumented insured variable,14 Aij is the wealth variable, A∗ is the

19

wealth threshold around which coping strategies split, and superscripts ` and u indicate the

parameter vectors for the lower and upper wealth regimes respectively. Our primary interest

is in βlS and βu

S, including testing for whether the two parameters are different. In the next

sections, we explore two alternative methods for performing this test and identifying the

critical wealth threshold. The first draws on the relatively rich empirical literature regarding

the northern Kenya livestock system which identifies a relatively precise prior on the value

of A∗. The second approach more conservatively employs threshold estimation techniques

(based on Caner and Hansen, 2004) to simultaneously estimate both A∗ and the parameter

vectors of interest.

Threshold based on Prior Knowledge

The livestock-based economic system in the northern Kenyan rangelands, the location

of this study, have been the subject of substantial empirical investigation. Three studies

stand out as using distinct methods to identify a critical asset threshold where economic

behavior changes. Lybbert et al. (2004) use panel data to non-parametrically estimate a

threshold around which accumulation strategies bifurcate, with households below the esti-

mated threshold heading to a low-level, poverty trap, equilibrium, and those above heading

towards a higher level asset equilibrium. Santos and Barrett (2011) hypothesize that informal

credit and insurance transactions will be sensitive to the presence of a poverty trap and test

indirectly for the presence of a critical asset threshold by examining data on informal trans-

actions. Finally, Toth (2015) hypothesizes that if a poverty trap exists in this environment,

it would be driven by a non-convexity in the production function due to minimum herd size

necessary to undertake high return seasonal herd migration. He then examines production

data directly to identify the existence of such a minimum scale for seasonal migration.

While distinctive in their approaches, these studies all detect asset thresholds in the

neighborhood of approximately 8-12 tropical livestock units.15 Based on these findings, we

employ the mid-point of this range (10) as the asset threshold. Consistent with the theoretical

20

analysis of Ikegami et al. (2016) and Carter and Janzen (2015), we anticipate a conservative

asset smoothing strategy as households approach this tipping point. However, given the

severity of risk in the system (where single drought events can destroy upwards of 40%

of household livestock assets), Ikegami et al. (2016) and Carter and Janzen (2015) predict

continued asset smoothing behavior by households just above the tipping point as they seek

first to reduce their vulnerability, before later shifting behavior toward less conservative

consumption smoothing. Using the empirical and theoretical literature as our guide, we thus

propose to test model 3.3 using a value of A∗ = 15 as the behavioral threshold. Sensitivity to

the use of this pre-established threshold is addressed through the more conservative threshold

estimation approach employed in the following subsection.

Returning to Table 5, columns 2 and 3 display the impact of insurance on quarter 4

consumption smoothing and asset protection for households above and below a threshold

value of 15 tropical livestock units. The second panel shows the estimated average insurance

impact (61 percentage point reduction) on livestock sales masks a larger impact for wealthier

households (a 71 percentage point reduction) and a smaller reduction (41 percentage points)

for less wealthy households. Impacts for both groups are statistically significant, although

a z-test for a difference in coefficients above and below the threshold value is marginally

insignificant.

As shown in the top panel of Table 5, the differential impact of insurance is sharper

for meal reduction. The average effect of a statistically insignificant 12 percentage point

reduction (column 1) is shown to be the result of a statistically significant 31 percentage

point reduction for less wealthy households and a near-zero impact for households above the

15 TLU wealth level. A z-test reveals this difference in estimated impacts on meal reduction

above and below the threshold is statistically significant at the 5% level.16

These heterogeneous impact results indicate that insurance works differently for house-

holds of different wealth levels. Insurance allows secure households to reduce their reliance

on asset sales as a consumption smoothing coping mechanism. For the poor and vulnerable,

21

insurance has a modest impact on livestock sales and a strong impact on meal reduction as

an asset smoothing coping strategy.

Estimated Threshold

While the existing literature provides surprisingly consistent guidance on the location of

an asset threshold in the pastoral regions in the Horn of Africa, we can also use our data

to directly test for the existence of a threshold. Some earlier empirical literature (Carter et

al. (2007) and Carter and Lybbert (2012)) relied on Hansen (2000) to test for the presence

of an asset threshold as it relates to differentiated coping strategies. Here, because our key

variable (being insured) is instrumented using discount coupon treatments from a randomized

controlled trial, we rely on the approach of Caner and Hansen (2004) who develop GMM

methods to extend Hansen (2000) to the case of an endogenous explanatory variable for

which a set of valid instruments exist. As in the Hansen (2000) paper, the Caner and

Hansen estimation method tests each possible threshold value A (splitting the sample into

upper and lower regimes around each A) and devises a statistic to test whether splitting

the sample at A is statistically significant as compared to a null hypothesis that no such

threshold exists.

Figure 4.2 graphs the relevant test statistic against each possible asset value shown on

the horizontal axis. The dashed horizontal line in the figure is the 90% critical value of the

test statistic, and for values of the test statistic below that critical level, we can reject the

null. The preferred estimate of the threshold is the asset level with the lowest value of the

test statistic. When the data indicate a sharp discontinuity, we would expect to see a sharp

v-shaped pattern in the test statistic. A less precise discontinuity might yield a flatter, or

u-shaped relationship.

As can be seen in Figure 4.2a, the test statistic for reduced consumption reveals a rel-

atively sharp discontinuity, with candidate threshold values ranging from about 8 to 12

tropical livestock units. The best estimate is a threshold of 9.3 TLU, as reported in the top

22

panel of Table 5. While somewhat lower than the pre-established threshold of 15 TLU de-

rived from prior literature, splitting the data at this point reveals a much stronger difference

in the impact of insurance between lower and higher wealth households.

Figure 4.2b indicates that the discontinuity in the regression regimes for livestock sales

is much less sharply identified. Statistically significant asset threshold values span the range

from about 14 to 25 TLU, with a best estimate of 22.4 TLU. The inter-regime insurance

impact is again stronger under the estimated threshold (a 42 percentage point difference

versus a 30 percentage point difference under the threshold based on prior knowledge). It is

surprising that this best estimate is higher than the best estimate of the threshold at which

meal reduction changes. However, as seen clearly in Figure 4.2b, the available data do not

pin down sharply livestock sales threshold, signalling that some unobserved heterogeneity

is at work. The theoretical analyses of Ikegami et al. (2016) and Carter and Janzen (2015)

hypothesize that skill differentials are a potentially important source of such heterogeneity

indicating that high skill, lower asset households may be more reluctant to sell assets than

their lesser skilled counterparts.

The asymptotic standard errors reported in columns 4 and 5 of Tables 5 are estimated

using the methods developed by Caner and Hansen (2004). These robust standard errors

indicate insurance significantly reduces reliance on livestock sales for both lower and upper

regime households, but only reduces reliance on meal reduction for lower regime house-

holds. While inference based on these standard errors is asymptotically valid (as sample size

approaches infinity), Caner and Hansen note that the small sample performance of these

standard errors is somewhat poor. Intuitively, they note the asymptotic standard errors do

not account for the fact that the threshold is itself a noisy estimate (again, especially in

small samples). They thus suggest a statistically conservative approach in which the slope

coefficients are calculated for every candidate threshold value whose likelihood ratio statistics

indicates rejection of the null at the 80% level. In the case of the meal reduction regression,

this procedure involves calculating the confidence interval for every candidate threshold value

23

between approximately 8 to 12 TLU. The suggested conservative confidence interval is the

union of the set of confidence intervals (or extreme values) that emerges from this exercise.

The third rows in both the top and bottom panels of Table 5 give the 90% interval estimates

for the impact of insurance on coping. For the lower wealth group, this confidence interval

includes zero, despite a relatively small asymptotic standard error.

Especially in the case of the livestock sales regression, calculation of these asymptotically

conservative confidence intervals requires estimation across a broad domain of candidate

threshold values. For some of these values (those at the extreme ends of the domain), the

underlying GMM estimation breaks down for reasons of collinearity when location fixed

effects are included.17 While this problem did not emerge for the meal reduction regression

with its more precisely estimated threshold, it did prove to be a problem for the livestock

sales regression. We thus report results in the bottom panel of Table 5 only for the case of no

location fixed effects. While not ideal, we find that inclusion or exclusion of location effects

has no noticeable effect on the estimated parameters of the model with the pre-determined

threshold. For both low and high wealth groups, the asymptotically conservative confidence

intervals exclude zero, indicating statistically significant heterogeneous impacts even after

using the more conservative approach.

In summary, these results indicate that receipt of an insurance payment allows house-

holds to reduce their reliance on often costly autarkic coping strategies. For modestly better

off households, insurance allows households to continue to defend their usual consumption

standard without relying on costly livestock sales at depressed prices. For less well-off house-

holds, insurance allows them to better smooth consumption while holding on to stocks of

productive assets.

4 Conclusion

When adverse shocks strike in developing countries, poor households are often forced to

24

choose between drawing down consumption or their physical productive assets. As Hod-

dinott (2006) points out, consumption is an input into the formation and maintenance of

human capital, implying that regardless of the choice made, uninsured risk may have long-

term consequences that undermine future productivity.18 In this paper we assess whether

insurance can function as a safety net, protecting assets and smoothing consumption, thereby

improving the human capital of future generations. Our findings suggest that IBLI insurance

payouts in Marsabit district of northern Kenya during the drought of 2011 provided substan-

tial immediate benefits to insured households. On average, insured households who receive

a payout are much less likely to sell livestock, improving their chances of recovery. Insured

households on average also expect to maintain their current food consumption, rather than

reduce meals as their uninsured neighbors do.

Consistent with the rich theoretical literature on ex post risk coping strategies (e.g.

Townsend, 1995; Kazianga and Udry, 2006), we also show that households in our sample

behave quite differently depending on their asset holdings. Using the Caner and Hansen

(2004) threshold estimator, we cannot reject at the 1% level the hypothesis that there are

two, quite distinctive behavioral regimes, with distinctive insurance impacts. Livestock-

poor households were more likely to smooth assets and destabilize consumption, whereas

livestock-rich households were more likely to smooth consumption during the 2011 drought.

This behavior is consistent with a number of theoretical perspectives.

These findings indicate that simply estimating the average effect of insurance masks

an interesting heterogeneous impact of insurance. The threshold-disaggregated estimates

show that insurance helps stop the households most likely to give up productive assets from

reducing their asset base, otherwise harming the household’s future income-earning potential.

In addition, insurance helps prevent those households most likely to reduce consumption

from doing so, thereby protecting vulnerable household members from undernutrition and

malnutrition, and improving the human capital of future generations. Considered jointly,

these impacts imply that insurance functions as a flexible safety net, allowing smoothing

25

of consumption and nutrition, while preserving productive assets and future livelihoods.

In this way, insurance promotes asset smoothing without having the deleterious long term

consequences of destabilized consumption.

These results come at a critical time for policymakers. In recent years we have observed

a push from development agencies to scale up microinsurance pilots with the goal of reaching

a larger number of households. This push has transpired in spite of an incomplete under-

standing of microinsurance impacts. The results presented here provide some of the first

empirical evidence that insurance can improve outcomes when negative strikes occur. We

recognize that our main results are based on immediate expectations regarding a specific

insurance pilot project, and are therefore not immediately generalizable. Indeed, further

impact analyses will help to generalize the results more broadly. However, this research

provides an important first step. If the declared intentions of pastoralists in northern Kenya

closely follow their true behavior, then the highly anticipated long term positive welfare

impacts of IBLI and other similar microinsurance projects are likely to be observed in the

near future.

26

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31

Notes

1 Classic studies of consumption smoothing include (Deaton, 1991; Paxson, 1992; Rosenzweig and Wolpin,

1993; Morduch, 1995; Townsend, 1995; Fafchamps et al., 1998; Gertler and Gruber, 2002)

2 The outcomes of undernutrition and malnutrition are well known. In children, these conditions can

lead to muscle wastage, stunting, increased susceptibility to illness, lower motor and cognitive skills, slowed

behavioral development, and increased morbidity and mortality (Martorell, 1999). Those that do survive

suffer functional disadvantages as adults, including diminished intellectual performance, work capacity and

strength. For example, Alderman et al. (2006) show persistent effects of drought shocks in Zimbabwe that

caused lower height-for-age scores and lower educational outcomes, presumably due to lower consumption.

In women, undernourishment during childhood can be the cause of lower adult body mass, which means

increased risk of delivery complications and lower birthweights for the next generation (Martorell, 1999).

These outcomes set the stage for a pernicious intergenerational cycle of undernutrition and its destructive

effects.

3 More recent evidence presented by Jensen et al. (2016) suggests that basis risk may more important

than originally though - the authors estimate that IBLI policyholders are left with an average of 69% of their

original risk due to high loss events.

4 The IBLI contract is expressed in tropical livestock units (TLUs). A goat or sheep is equal to .1 TLU,

cattle are equal to a single TLU, and a camel is equal to 1.4 TLU.

5 A single TLU (one cow or ten goats) in a typical non-drought season is valued at approximately 20,000

Kenyan Shillings.

6 A non-livestock asset index was constructed from the first principle component using factor analysis.

Variables used to generate the asset index include housing characteristics (such as materials used in the wall

or for flooring in the house), cooking appliances, access to water, and possession of large assets such as a

motorbike, boat, sewing machine, grinding mill or television.

7 Given that households lost on average 35% of their productive wealth in the 2011 drought, it is perhaps

not surprising to see consumption cut-backs by even wealthier households who must have experienced some

drop in their permanent income due to the shock. Moreover, as discussed in footnote 2 above and in the

appendix, we would expect even wealthier households to at least partially destabilize consumption given that

livestock prices drop sharply in the face of drought and that shocks are modestly autocorrelated.

8The Stable Unit Treatment Value Assumption (SUTVA, Rubin (1978)) requires that potential outcomes

are unrelated to the treatment status of others. In this context it seems unlikely that receipt of a coupon by

someone else in the same community would affect one’s decision to insure, especially as insurance discount

32

coupons were distributed privately. One might also worry that that an insured household induced to buy

insurance with a coupon might share an insurance payout with an uninsured peer who did not receive a

coupon. This kind of spillover externality would bias our results downward (the comparison group would be

better off as a result of insurance), such that our results are conservative.

9 Because households in different geographic locations face different risks and hold their wealth in different

species–e.g., more camels in the more arid locations–location fixed effects have a potentially important role to

play. We note that demand is actually quite complicated, and leave a more rigorous treatment and discussion

of insurance demand to other work. Janzen et al. (2015) provide a theoretical discussion and Jensen et al.

(2014) provide a detailed empirical analysis in this setting.

10 Heckman selection methods yield an estimate of a 27% point drop, while matching methods yield an

average impact of a 30% point drop. Full results are available from the authors.

11 Both Heckman and matching methods estimated the average impact of insurance to be a 37% point

drop in reliance upon meal reductions as a way to cope with the drought.

12Results available upon request from the authors show that this same pattern of wealth-differentiated

coping strategies is visible when the third quarter data are used to estimated threshold models akin to those

developed below for the fourth quarter data.

13One could worry that respondents may have exaggerated the impact that insurance was going to have on

their coping strategies in quarter 4 in order to please the interview team. This concern is no different than

the worry that respondents might similarly exaggerate the impact of a program by exaggerating behavior

that has already taken place. Were this to have happened in this case, estimated impacts will overstate

the true impacts, irrespective of whether data reflect intended or already realized behaviors. However, such

exaggeration should have no particular impact on this paper’s primary findings that coping strategies, and

the impact of insurance upon them, is differentiated by household wealth level.

14We do not assume a threshold for the first stage equation.

15 Santos and Barrett (2011) estimate a threshold of 7-10 tropical livestock units per household. Lybbert et

al. (2004) estimate the threshold to be between 10-15 tropical livestock units per household. Toth (2015) finds

an estimated threshold of 5 tropical livestock units per adult male, which can be converted to approximately

7.5 tropical livestock units per household.

16 Intuitively, the more hypotheses we check, the higher the probability of making a Type I error. The

Bonferroni-Holm correction is a conservative approach to address this issue of a familywise error rate. We

employ the Bonferroni-Holm correction assuming two hypotheses tests (heterogeneous impacts for low and

high wealth households) using the calculated p-values for the threshold-disaggregated insurance impact

coefficients. Using this method, the estimated heterogeneous impacts remain statistically significant.

33

17 In any given location, there is a minimum and maximum herd size observed in the data. At more

extreme threshold values, all observations within a location may fall into a single regime, causing estimation

to break down.

18 Specifically, Hoddinott suggests, “The true distinction lies in households’ choices regarding what type

of capital - physical, financial, social or human (and which human) - that they should draw down given an

income shock.”

34

Figure 4.1: Timeline of IBLI Sales, Surveys, Payouts and Standardized NDVI

2009 2010 2011

Household    Survey  R3  

and    Index  Triggered:  IBLI  Payouts  Made  

Standardized  NDVI  

-­‐1.5  

-­‐1  

-­‐0.5  

0  

0.5  

1  

1.5  

2  

IBLI    Sale  

Household    Survey  R2  

Household    Survey  R1  

IBLI    Sale     Q3   Q4  

(Avg)  

35

Figure 4.2: Significance Test for Asset Threshold in Coping Strategies

(a) Coping via Reduced Consumption

(b) Coping via Livestock Sales

36

Table 1: General Summary Statistics

By Insurance Purchase By Discount CouponDifference Received No Difference

Variable Insured Uninsured in Means Coupon Coupon in MeansPermanently Settled .50 .48 -.025 .50 .46 -.042(dummy=1 if true) (.04) (.02) (.045) (.02) (.03) (.040)Livestock Owner .95 .95 -.001 .94 .96 .016(owns at least some livestock) (.02) (.01) (.020) (.01) (.01) (.017)Number of TLU Owned 16.22 18.86 2.64 18.07 18.49 .420(Oct. 2010) (1.40) (1.29) (2.42) (1.32) (1.69) (2.15)TLU Owned per capita 2.70 3.54 .85* 3.19 3.59 .404(Oct. 2010) (.24) (.25) (.457) (.22) (.37) (.407)Number of TLU Lost 7.67 7.53 -0.137 7.66 7.40 -.265(between Oct. 2010-2011) (.49) (.90) (1.00) (.54) (.69) (.884)Expected TLU Losses 6.99 7.43 .442 7.71 6.68 -1.03(between Oct. 2011-2012) (.61) (.36) (.73) (.39) (.51) (.645)Farmer .25 .21 -.041 .21 .25 .034(farms any agricultural land) (.03) (.02) (.038) (.02) (.03) (.033)Non-livestock Asset Index 2011 .15 .00 -.150 -.02 .14 .161*(from factor analysis) (.10) (.05) (.098) (.05) (.08) (.086)Credit Constrained .42 .37 -.039 .42 .34 -.078**(difficulty acquiring a loan) (.04) (.02) (.044) (.02) (.03) (.039)Borrower .33 .36 .038 .36 .34 -.024(Borrowed in past year) (.04) (.02) (.043) (.02) (.03) (.038)Savings .18 .16 -.017 .14 .21 .072**(Any current household savings) (.03) (.02) (.034) (.02) (.03) (.030)Amount of savings 28890 71498 42608 40353 83441 43088(If any savings) (11942) (18337) (32063) (9906) (27600) (27988)Lender .07 .06 -.004 .07 .06 -.008(Loaned any money) (.02) (.01) (.022) (.01) (.01) (.020)HSNP .32 .43 .103** .42 .37 -.057(cash transfer recipient) (.04) (.02) (.044) (.02) (.03) (.039)Dependency Ratio .50 .54 .047*** .54 .53 -.009(<15yrs, >55yrs, disabled, chronic ill) (.01) (.01) (.017) (.01) (.01) (.015)Cell Phone .45 .51 .058 .48 .53 .045(uses at least monthly) (.04) (.02) (.045) (.02) (.03) (.040)Social groups .86 .70 -.161** .78 .66 -.121*(Number of groups participating in) (.06) (.04) (.077) (.04) (.05) (.068)Muslim .26 .26 .003 .27 .23 -.040(dummy=1 if true) (.04) (.02) (.040) (.02) (.03) (.035)Christian .30 .42 .117*** .38 .40 .018(dummy=1 if true) (.04) (.02) (.045) (.02) (.03) (.040)Literate .09 .12 .033 .11 .13 .021(write a simple letter in English) (.02) (.01) (.029) (.02) (.02) (.026)Years of Education .76 1.18 .416 1.03 1.16 .131(household head) (.21) (.15) (.294) (.15) (.21) (.259)Risk-taking .24 .29 .049 .28 .28 -.007(dummy=1 if risk-taking) (.03) (.02) (.041) (.02) (.03) (.037)Risk-moderate .50 .45 -.054 .45 .48 .032(dummy=1 if risk-moderate) (.04) (.02) (.046) (.02) (.03) (.040)Age 46.22 49.15 2.93** 47.06 50.87 3.803***(household head) (1.22) (.74) (1.49) (.75) (1.14) (1.313)Female .42 .36 -.059 .37 .37 -.001(household head) (.04) (.02) (.044) (.02) (.03) (.039)

Observations 161 514 426 249

Standard errors, including the standard errors of the difference in means, are reported in parentheses.For the difference in means tests: *** p<0.01, ** p<0.05, * p<0.1.

37

Table 2: Summary Statistics for Potential Instruments

By Insurance PurchaseDifference

Variable Insured Uninsured in Means

Received IBLI Discount Coupon .88 .55 -.321***(dummy=1 if true) (.03) (.02) (.042)Value of IBLI Discount Coupon 23.79 16.46 -7.33***(of: 0, 10, 20, 30, 40, 50, 60) (1.83) (.96) (1.98)

Observations 161 514

Standard errors, including the standard errors of the difference in means, are reported in parentheses.For the difference in means tests: *** p<0.01, ** p<0.05, * p<0.1.

38

Table 3: Consumption and Asset Smoothing in Northern Kenya

By Livestock Wealth By Insurance PurchaseAverage Lowest Highest Difference Difference

Variable Response Quartile Quartile in Means Insured Uninsured in Means

Asset Smoothing

Q3 Probability Reduce Meals (%) 72 82 61 21*** 64 75 10.9***(prior to payout) (1.7) (3.0) (3.8) (4.9) (3.8) (1.9) (4.0)Q4 Probability Reduce Meals (%) 62 72 51 21*** 33 71 37.9***(after receiving payout) (1.8) (3.5) (4.0) (5.3) (3.7) (2.0) (4.1)

Consumption SmoothingQ3 Probability Sell Livestock (%) 29 12 44 32*** 34 28 -.053(prior to payout) (1.7) (2.6) (3.9) (2.5) (3.7) (1.9) (4.1)Q4 Probability Sell Livestock (%) 27 12 42 30*** 11 32 20.7***( after receiving payout) (1.7) (2.6) (3.9) (4.7) (2.5) (2.0) (3.9)

Observations 675 163 161 161 514

Standard errors, including the standard errors of the difference in means, are reported in parentheses.For the difference in means tests: *** p<0.01, ** p<0.05, * p<0.1.

39

Table 4: Demand for Insurance: First Stage Linear Probability Model Selection Regression

(1) (2)Received IBLI discount coupon 0.29∗∗∗ 0.30∗∗∗

(0.061) (0.053)Value of IBLI discount coupon -0.0011 -0.0012

(0.0013) (0.0012)Years of education (head) -0.016∗∗ -0.019∗∗

(0.0063) (0.0067)Risk-taking 0.0021 -0.023

(0.037) (0.034)Risk-moderate 0.015 0.017

(0.023) (0.028)Non-livestock asset index 0.059∗ 0.050

(0.028) (0.029)TLU Owned -0.0011 -0.0011∗∗

(0.00064) (0.00040)TLU losses in past year 0.0025∗ 0.0019

(0.0014) (0.0017)Expected TLU losses -0.0012 -0.0017

(0.0017) (0.0021)Credit Constrained 0.025 0.033

(0.035) (0.044)Household currently has savings 0.038 0.017

(0.039) (0.044)Age (head) -0.0016∗ -0.0023∗∗

(0.00080) (0.00084)Number of group memberships 0.0048 0.034

(0.013) (0.025)Constant 0.0068 0.18∗∗∗

(0.055) (0.057)Location fixed effects yes noObservations 627 627R2 0.245 0.125Standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

40

Table 5: Insurance Impacts on ex post coping strategies

Pre-established Threshold Estimated Threshold(1) (2) (3) (4) (5)

Average Low High Low High

Panel A: Meal Reduction<15 >15 <9.3 >9.3

Insured -0.12 -0.31** -0.05 -0.49** 0.004(0.11) (0.16) (0.03) (0.23) (0.17)

- - - [-.91, .03] [-.36, .30]

Location fixed effects yes yes yes yes yesObservations 627 381 246 303 324R-squared 0.21 0.26 0.23 - -Test equality of coefficients 1.65** 1.72**

Panel B: Livestock Sales<15 >15 <22.4 >22.4

Insured -0.61*** -0.41** -0.71*** -0.54*** -0.96***(0.16) (0.20) (0.16) (0.17) (0.24)

- - - [-.85, -.11] [-1.46, -.47]

Location fixed effects yes yes yes no noObservations 627 381 246 459 168R-squared 0.12 0.13 0.24 - -Test equality of coefficients 1.14 1.42*Standard errors in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

Columns (1) - (3) report cluster robust standard erros, while columns (4) & (5) report asymp-totic standard errors and asymptotically conservative 90% confidence intervals in brackets. Allestimations include the following control variables: number of years of education (household head),a dummy variable for risk-taking, a dummy variable for risk-moderate, non-livestock asset index,number of TLU owned, number of TLU losses in the past year, number of expected TLU losses, adummy variable for credit constrained, a dummy variable for if the household currently has savings,age (head), number of group memberships.

41